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of Technology (TU Delft) is hiring a doctoral candidate (4 years) on the subject of “statistical surrogate models of flexible energy systems”. Numerical models are at the core of the successful operation of
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) on the other hand. We use tools from statistical physics, information theory and non-linear dynamics to understand the how well a particular system responds to a stimulus, and how this stimulus is processed
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challenges in such large-scale distributed networks i.e., the low-cost sensing, decentralized statistical inference, distributed control and online decision making. These distributed systems will have to fuse
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of this position is the theoretical foundation of statistical machine learning, with applications to satellite-based imageries. Candidates are required to have a strong background in mathematical statistics, signal
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regeneration methods. Explore laboratory data using statistics and adsorption models to understand underlying trends, explore adsorption mechanisms and predict performance. Investigate sorbent fouling and
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, Statistics/Mathematics, Data Science, International Development, or a related field (essential). Experience working with large datasets, models and (geospatial) programming skills (e.g. preferably Python or R
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to the research topic; A strong mathematical background in optimisation, statistical learning, linear algebra and probability; A solid understanding of machine and deep learning; An experience in programming in
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the IVORY doctoral network, this PhD position aims to disentangle the strengths and weaknesses of two types of analytical methods in road safety research i.e. statistical and econometric methods and machine
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, atmospheric stability, and urban setting)? How can we best represent this variability in outcome with a statistical model and take its inverse? Is such framework adaptive and generalizable to changes in urban
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, problem-solving, programming, and analytical skills. Strong interest and experience in conducting empirical research (e.g., user studies), knowledge in inferential statistics and qualitative methods (e.g